def merge(q1, q2):
''' merge an n dimensional query and an m dimensional query into an (n+m) dimensional query '''
ans = ndRangeUnion()
for (lb1, ub1, wgt1) in q1.ranges:
for (lb2, ub2, wgt2) in q2.ranges:
ans.addRange(lb1 + lb2, ub1 + ub2, wgt1 * wgt2)
return ans
def __init__(self,
shape_list,
domain_shape,
size,
seed=9001,
pretty_name='random range'):
self.init_params = util.init_params_from_locals(locals())
self.shape_list = copy.deepcopy(shape_list)
self.seed = seed
self.pretty_name = pretty_name
self._size = size
prng = numpy.random.RandomState(seed)
if shape_list == None:
shapes = randomQueryShapes(domain_shape, prng)
else:
prng.shuffle(self.shape_list)
shapes = itertools.cycle(
self.shape_list) # infinite iterable over shapes in shape_list
queries = []
for i in range(size):
lb, ub = placeRandomly(next(shapes), domain_shape,
prng) # seed must be None or repeats
queries.append(ndRangeUnion().addRange(lb, ub, 1.0))
super(RandomRange, self).__init__(queries, domain_shape)
def __init__(self, domain_shape, pretty_name='total'):
self.pretty_name = pretty_name
if type(domain_shape) is int:
domain_shape = (domain_shape, )
lb = tuple(0 for _ in domain_shape)
ub = tuple(x - 1 for x in domain_shape)
q = ndRangeUnion().addRange(lb, ub, 1.0)
super(self.__class__, self).__init__([q], domain_shape)
def __init__(self, domain_shape, weight=1.0, pretty_name='identity'):
self.init_params = util.init_params_from_locals(locals())
self.weight = weight
self.pretty_name = pretty_name
if type(domain_shape) is int:
domain_shape = (domain_shape, )
indexes = itertools.product(*[list(range(i)) for i in domain_shape
]) # generate index tuples
queries = [ndRangeUnion().addRange(i, i, weight) for i in indexes]
super(self.__class__, self).__init__(queries, domain_shape)
def __init__(self, domain_shape_int, pretty_name='prefix 1D'):
self.init_params = util.init_params_from_locals(locals())
self.pretty_name = pretty_name
queries = [
ndRangeUnion().add1DRange(0, c, 1.0)
for c in range(domain_shape_int)
]
super(self.__class__, self).__init__(queries, (domain_shape_int, ))
def kronecker(workloads):
''' merge a list of low dimensional workloads into a single high dimensional workload '''
zq = ndRangeUnion().addRange(tuple(), tuple(), 1.0)
zero = Workload([zq], tuple())
return reduce(kron, workloads, zero)